About
About R-HTA in LMICs
R-HTA is a consortium focused on promoting the use of R for health economics and health technology assessment (HTA). While its core members are from the Americas and the UK—representing government institutions like NICE, academia, and industry—R-HTA aims to serve a global community of analysts.
Our Vision
To empower LMIC (low- and middle-income countries) health systems with cutting-edge, reproducible, and accessible analytical tools for HTA using the R language.
What is R-HTA in LMICs?
The R-HTA in LMICs chapter is the regional arm of the R-HTA consortium. Its mission is to: - Introduce R as a viable alternative to costly software - Support LMIC analysts and institutions in adopting R for HTA - Share tailored educational resources and workshops
💡 We believe that open-source tools like R can help bridge the digital and economic divide in health analysis.
Why Focus on LMICs?
HTA in LMICs is often constrained by: - Heavy reliance on proprietary software like Microsoft Excel and TreeAge - Limited budgets and access to advanced tools - Lack of hands-on, practical R training for HTA analysts
Meanwhile, health systems in LMICs are rapidly evolving, especially under the drive toward Universal Health Coverage (UHC). This creates a growing need for: - Complex modelling (e.g. oncology, rare diseases) - Scalable and reproducible tools - Local capacity-building
What We Offer
Our LMIC chapter delivers: - Introductory workshops: Covering R basics and essential HTA packages - Advanced tutorials: Building cost-effectiveness models and interactive apps using Shiny - Free resources and code: All available on our GitHub page
Why R?
✅ Scalable
R handles complex HTA models more efficiently than spreadsheet-based tools.
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✅ Reproducible
With Git, RMarkdown, and Shiny, models can be: - Shared with version control - Easily updated - Delivered as interactive tools
✅ Open and Community-Driven
R’s vibrant health economics ecosystem includes: - BCEA - heemod - hesim - dampack
Join Us
We welcome collaborators and learners alike—join us in building a stronger foundation for HTA in LMICs using R.